Data warehouse and online analytical processing (“OLAP”) systems may be used to perform various functions related to data mining, reporting, and forecasting. OLAP systems may permit multidimensional analysis of data typically obtained from transactional systems, such as relational databases, and loaded into a multidimensional cube structure. Data points, such as various aggregate values, may be calculated within the n-dimensional cube structure at each intersection of the various dimensions it contains. Accordingly, the process of populating a multidimensional cube structure may involve significant amounts of computation. In addition, the n-dimensional cube may be updated on a periodic basis to incorporate new data. Updating the n-dimensional cube may involve recomputing the data points at each intersection of its dimensions. The recomputation may be even more burdensome when new dimensions are to be added to the n-dimensional cube. Accordingly, these types of n-dimensional cube structures are not well suited to dynamic data environments.